Discriminating features learning in hand gesture classification
The advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More spe...
Main Authors: | Feng Jiang, Cuihua Wang, Yang Gao, Shen Wu, Debin Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2015-10-01
|
Series: | IET Computer Vision |
Subjects: | |
Online Access: | https://doi.org/10.1049/iet-cvi.2014.0426 |
Similar Items
-
Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model
by: Jianyong Li, et al.
Published: (2022-03-01) -
Kinect-based gesture interaction design method for smart terminal APP interface
by: Wang Hui, et al.
Published: (2024-01-01) -
3D Skeletal Joints-Based Hand Gesture Spotting and Classification
by: Ngoc-Hoang Nguyen, et al.
Published: (2021-05-01) -
A Review of the Hand Gesture Recognition System: Current Progress and Future Directions
by: Noraini Mohamed, et al.
Published: (2021-01-01) -
The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals
by: Aly Medhat Moslhi, et al.
Published: (2024-02-01)